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2019.11.35

def train(net, train_iter, loss, epochs, lr):    trainer = torch.optim.Adam(net.parameters(), lr)    for epoch in range(epochs):        for X, y in train_iter:            trainer.zero_grad()            l = loss(net(X), y)            l.backward()            trainer.step()        print(f'epoch {epoch + 1}, '              f'loss: {d2l.evaluate_loss(net, train_iter, loss):f}')net = get_net()train(net, train_iter, loss, 5, 0.01)

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